Free Webcast: The Power of Machine Learning and Graphs - March 1, 2017

Graphs and Machine Learning have long been a focus for Franz
Inc. and currently we are collaborating with a number of companies to
deliver the ability to understand possible future events based on a
company's internal as well as externally available data. By combining
machine learning, semantic technologies, big data, graph databases and
dynamic visualizations we will discuss the core components of a
Cognitive Computing platform.

Cognitive Computing Platforms are a growing phenomenon that have
been shown to add significant value to the Enterprise. IBM's Watson is
just one of the more well known examples. The power of these platforms
is that you not only base your enterprise decisions on what is in your
structured enterprise data warehouse, but you also mine the
unstructured data. In addition, the platform combines proprietary and
public knowledge in the form of vocabularies, taxonomies, ontologies,
and linked open data with a powerful layer of machine learning
technologies. The resulting analytics are pushed back into the core
knowledge corpus resulting in a learning system that continually tunes
desired metrics...

Knowledge creation via Cognitive Probability Graphs stems from the
capability to combine the probability space (statistical inference on
patient data) with a knowledge base of comprehensive industry
terminology systems. Cognitive Probability Graphs are remarkable not
just because of the possibilities they engender, but also because of
their practicality. The confluence of knowledge via machine learning,
semantics, visual querying, graph databases, and big data not only
displays links between objects, but also quantifies the probability of
their occurrence. We believe this approach will be transformative
across numerous business verticals...

Enterprise Data World Presentation: Developing an Advanced Analytics Capability on an Enterprise Data Lake, April 4

Enabling the Data Lake for scalable and extensible analytics with
the ultimate goal of developing a learning system is rapidly taking
shape for the Enterprise. Until recently big data was focused on
processing massive amounts of simple, flat data. But now, there is a
requirement to fuse complex data to create more intelligent analytic
frameworks to achieve better business decisions. Adding advanced
analytics to a Data Lake to create a scalable knowledge-based
analytics platform for pattern recognition, classification, predictive
modeling, and simulations is rapidly developing with use cases in
Fraud Detection, Healthcare, E-commerce, Intelligence, and more...

In healthcare there is a growing desire for patients to own their
medical records. Interestingly, this desire is not coming from
patients, it's based on the view from medical practitioners that
patient care and quality of life is directly influenced by the ability
of patients to access and utilize their data. This view is core to the
Precision Medicine Initiative, a White House program for personalizing
healthcare treatment for individuals and groups that have historically
been underrepresented. Its mission statement points out that "Success
will require that health data is portable, that it can be easily
shared between providers, researchers, and most importantly, patients
and research participants"...

Linkurious is an Enterprise class tool for visualizing graph
data. Linkurious is a partner of the International Investigative
Journalist Consortium (ICIJ) since the Swiss Leaks scandal. ICIJ
network of 370 journalists is using Linkurious to investigate the
Panama Papers. Integration with AllegroGraph is currently
underway. The following images are of Linkurious screenshots
displaying graph data stored in AllegroGraph. (Click the images to enlarge)

Fixed: In 6.3.3, dashes were not drawn where node labels are wrapped
to multiple text lines and also at their start and end. This included
not drawing the minus sign at the beginning of numeric literals.

There's a new "View | Web Interaction" child menu where all but the
first command are new. Two of them will tell your web browser to
search either the web or Wikipedia on the selected node's label. The
other two will attempt to import either descriptions or images from
DBpedia or Wikipedia (respectively) based on the selected node's
label, and display them as linked nodes when any are found.

On the Mac, nodes can now be highlighted and unhighlighted by using
the Command key along with a left click (and also dragging to "lasso"
a group of nodes to highlight them). The standard gesture had not
worked because the Control key is used instead, and a
control-left-click on the Mac is interpreted as a logical right-click.

Various improvements when displaying images (pixmaps) on nodes: The
new option "Visual Graph Options | Node Labels | Maximum Initial Node
Pixmap Size" limits the initial size of a node that displays an image.
When resizing an image node, its width-to-height ratio will be
maintained to avoid distorting it. After resizing an image node you
can go back to the state just before resizing it, without going back
further. The icon in the lower-right corner of an image node is a
resizing icon to indicate that you can drag that corner to resize the
node. Fixed: Going back to an earlier state did not restore the
earlier size of resized nodes.

Fixed: Path-finding in Gruff 6.3.3 and earlier breaks on AllegroGraph
6.1.0 and later when the user does not have the privilege for
evaluating arbitrary code on the server.

Fixed: Doing a tree layout from a node that's connected with another
copy of itself could draw the tree layout incorrectly and then cause
an infinite recursion when moving the mouse over a node.

When selecting a predicate in the graphical query view, there's a new
option for selecting any of the "magic properties" that Franz provides
for enhancing SPARQL queries. (You may not be able to specify the end
values that some of them need, though, except by editing the generated
query text in the query view.)

User options are now saved automatically each time you explicity
modify an option, to avoid losing changes when Gruff is closed in a
way that it doesn't catch, such as with the close button in the title
bar on the Mac (and maybe on GTK generally), or if Gruff or the
machine should crash. Options are still saved at exit time as well.
They are also saved when you save a view or open or create a database.

The somewhat obscure new option "Visual Graph Options |
Constraint-Based Layout Options | Link to Node Ratio Limit" causes the
spring layout algorithm to be used even for small numbers of nodes if
the links-to-nodes ratio is high enough that the constraint-based
algorithm is likely to take a while.

When a node is both selected and highlighted, two border colors are
used to indicate that it is both, rather than only red to indicate
that it's selected.

Fixed: In the store-opening dialog, if both the server and port are
filled in and you proceed to modify both of them, Gruff would hang for
a bit after you change one of them as it asked the mismatching server
and port for its catalogs. This request is now always done
asynchronously to avoid holding you up, though as a consequence you
may now see the dialog appear before it has filled in the lists of
catalogs and stores.

Graph databases, like AllegroGraph, are one of the new technologies encouraging a rapid re-thinking of the analytics landscape. By tracking relationships - in a network of people, organizations, events and data - and applying reasoning (inference) to the data and connections, powerful new answers and insights are enabled...

KRSTE.my (Knowledge Resource for Science and
Technology Excellence, Malaysia) is an initiative, based on
AllegroGraph, by MOSTI and spearheaded by MASTIC to address science
and technology issues and challenges faced by the community, the
ministry and the country. KRSTE.my is designed to be a Single Point
Access Facilities (SPAF) providing intelligent collaborative knowledge
management and learning services platform on Science and Technology
and Innovation. More info
here.

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